Overview

Dataset statistics

Number of variables12
Number of observations891
Missing cells866
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.7 KiB
Average record size in memory96.1 B

Variable types

Numeric5
Categorical7

Alerts

Name has a high cardinality: 891 distinct valuesHigh cardinality
Ticket has a high cardinality: 681 distinct valuesHigh cardinality
Cabin has a high cardinality: 147 distinct valuesHigh cardinality
Survived is highly overall correlated with SexHigh correlation
Sex is highly overall correlated with SurvivedHigh correlation
Age has 177 (19.9%) missing valuesMissing
Cabin has 687 (77.1%) missing valuesMissing
PassengerId is uniformly distributedUniform
Name is uniformly distributedUniform
Ticket is uniformly distributedUniform
Cabin is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2023-08-29 12:58:13.862548
Analysis finished2023-08-29 12:58:24.897252
Duration11.03 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-08-29T12:58:25.096397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2023-08-29T12:58:25.407631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Length

2023-08-29T12:58:25.665536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:25.897576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2023-08-29T12:58:26.109379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:26.381637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Braund, Mr. Owen Harris
 
1
Boulos, Mr. Hanna
 
1
Frolicher-Stehli, Mr. Maxmillian
 
1
Gilinski, Mr. Eliezer
 
1
Murdlin, Mr. Joseph
 
1
Other values (886)
886 

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry

Common Values

ValueCountFrequency (%)
Braund, Mr. Owen Harris 1
 
0.1%
Boulos, Mr. Hanna 1
 
0.1%
Frolicher-Stehli, Mr. Maxmillian 1
 
0.1%
Gilinski, Mr. Eliezer 1
 
0.1%
Murdlin, Mr. Joseph 1
 
0.1%
Rintamaki, Mr. Matti 1
 
0.1%
Stephenson, Mrs. Walter Bertram (Martha Eustis) 1
 
0.1%
Elsbury, Mr. William James 1
 
0.1%
Bourke, Miss. Mary 1
 
0.1%
Chapman, Mr. John Henry 1
 
0.1%
Other values (881) 881
98.9%

Length

2023-08-29T12:58:26.666855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15446
64.3%
Uppercase Letter 3645
 
15.2%
Space Separator 2735
 
11.4%
Other Punctuation 1899
 
7.9%
Close Punctuation 144
 
0.6%
Open Punctuation 144
 
0.6%
Dash Punctuation 13
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1958
12.7%
e 1703
11.0%
a 1657
10.7%
i 1325
8.6%
n 1304
8.4%
s 1297
8.4%
l 1067
 
6.9%
o 1008
 
6.5%
t 667
 
4.3%
h 517
 
3.3%
Other values (16) 2943
19.1%
Uppercase Letter
ValueCountFrequency (%)
M 1128
30.9%
A 250
 
6.9%
J 215
 
5.9%
H 203
 
5.6%
S 180
 
4.9%
C 172
 
4.7%
E 166
 
4.6%
W 143
 
3.9%
B 140
 
3.8%
L 129
 
3.5%
Other values (15) 919
25.2%
Other Punctuation
ValueCountFrequency (%)
. 892
47.0%
, 891
46.9%
" 106
 
5.6%
' 9
 
0.5%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2735
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19091
79.5%
Common 4935
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1958
 
10.3%
e 1703
 
8.9%
a 1657
 
8.7%
i 1325
 
6.9%
n 1304
 
6.8%
s 1297
 
6.8%
M 1128
 
5.9%
l 1067
 
5.6%
o 1008
 
5.3%
t 667
 
3.5%
Other values (41) 5977
31.3%
Common
ValueCountFrequency (%)
2735
55.4%
. 892
 
18.1%
, 891
 
18.1%
) 144
 
2.9%
( 144
 
2.9%
" 106
 
2.1%
- 13
 
0.3%
' 9
 
0.2%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2023-08-29T12:58:27.578198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:27.848469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4192
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4192
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

Distinct88
Distinct (%)12.3%
Missing177
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean29.699118
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-08-29T12:58:28.103453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4
Q120.125
median28
Q338
95-th percentile56
Maximum80
Range79.58
Interquartile range (IQR)17.875

Descriptive statistics

Standard deviation14.526497
Coefficient of variation (CV)0.48912219
Kurtosis0.17827415
Mean29.699118
Median Absolute Deviation (MAD)9
Skewness0.38910778
Sum21205.17
Variance211.01912
MonotonicityNot monotonic
2023-08-29T12:58:28.411839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
28 25
 
2.8%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
(Missing) 177
 
19.9%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-08-29T12:58:28.671874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2023-08-29T12:58:28.880576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-08-29T12:58:29.106658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2023-08-29T12:58:29.310745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
347082
 
7
CA. 2343
 
7
1601
 
7
3101295
 
6
CA 2144
 
6
Other values (676)
858 

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450

Common Values

ValueCountFrequency (%)
347082 7
 
0.8%
CA. 2343 7
 
0.8%
1601 7
 
0.8%
3101295 6
 
0.7%
CA 2144 6
 
0.7%
347088 6
 
0.7%
S.O.C. 14879 5
 
0.6%
382652 5
 
0.6%
LINE 4
 
0.4%
PC 17757 4
 
0.4%
Other values (671) 834
93.6%

Length

2023-08-29T12:58:29.606807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4808
79.9%
Uppercase Letter 652
 
10.8%
Other Punctuation 295
 
4.9%
Space Separator 239
 
4.0%
Lowercase Letter 21
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 151
23.2%
O 100
15.3%
P 98
15.0%
A 82
12.6%
S 74
11.3%
N 40
 
6.1%
T 36
 
5.5%
W 16
 
2.5%
Q 15
 
2.3%
I 11
 
1.7%
Other values (6) 29
 
4.4%
Decimal Number
ValueCountFrequency (%)
3 746
15.5%
1 689
14.3%
2 594
12.4%
7 490
10.2%
4 464
9.7%
6 422
8.8%
0 406
8.4%
5 387
8.0%
9 328
6.8%
8 282
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
a 6
28.6%
s 5
23.8%
r 4
19.0%
i 4
19.0%
l 1
 
4.8%
e 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 197
66.8%
/ 98
33.2%
Space Separator
ValueCountFrequency (%)
239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5342
88.8%
Latin 673
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 151
22.4%
O 100
14.9%
P 98
14.6%
A 82
12.2%
S 74
11.0%
N 40
 
5.9%
T 36
 
5.3%
W 16
 
2.4%
Q 15
 
2.2%
I 11
 
1.6%
Other values (12) 50
 
7.4%
Common
ValueCountFrequency (%)
3 746
14.0%
1 689
12.9%
2 594
11.1%
7 490
9.2%
4 464
8.7%
6 422
7.9%
0 406
7.6%
5 387
7.2%
9 328
6.1%
8 282
 
5.3%
Other values (3) 534
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-08-29T12:58:29.885077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2023-08-29T12:58:30.170659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct147
Distinct (%)72.1%
Missing687
Missing (%)77.1%
Memory size7.1 KiB
C23 C25 C27
 
4
G6
 
4
B96 B98
 
4
C22 C26
 
3
D
 
3
Other values (142)
186 

Length

Max length15
Median length3
Mean length3.5882353
Min length1

Characters and Unicode

Total characters732
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)49.5%

Sample

1st rowC85
2nd rowC123
3rd rowE46
4th rowG6
5th rowC103

Common Values

ValueCountFrequency (%)
C23 C25 C27 4
 
0.4%
G6 4
 
0.4%
B96 B98 4
 
0.4%
C22 C26 3
 
0.3%
D 3
 
0.3%
F33 3
 
0.3%
E101 3
 
0.3%
F2 3
 
0.3%
B20 2
 
0.2%
E67 2
 
0.2%
Other values (137) 173
 
19.4%
(Missing) 687
77.1%

Length

2023-08-29T12:58:30.481015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c23 4
 
1.7%
c27 4
 
1.7%
g6 4
 
1.7%
b96 4
 
1.7%
b98 4
 
1.7%
f 4
 
1.7%
c25 4
 
1.7%
f33 3
 
1.3%
e101 3
 
1.3%
f2 3
 
1.3%
Other values (151) 201
84.5%

Most occurring characters

ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 460
62.8%
Uppercase Letter 238
32.5%
Space Separator 34
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 72
15.7%
1 61
13.3%
3 59
12.8%
6 51
11.1%
5 45
9.8%
4 37
8.0%
8 37
8.0%
7 34
7.4%
9 33
7.2%
0 31
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494
67.5%
Latin 238
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 72
14.6%
1 61
12.3%
3 59
11.9%
6 51
10.3%
5 45
9.1%
4 37
7.5%
8 37
7.5%
34
6.9%
7 34
6.9%
9 33
6.7%
Latin
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size7.1 KiB
S
644 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
(Missing) 2
 
0.2%

Length

2023-08-29T12:58:30.714420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:30.967576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 644
72.4%
c 168
 
18.9%
q 77
 
8.7%

Most occurring characters

ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 889
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 889
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Interactions

2023-08-29T12:58:21.926445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:15.167243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:17.160536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:18.989070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:20.241534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:22.295739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:15.533081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:17.528245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:19.224934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:20.484991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:22.634629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:15.961423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:17.917781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:19.471017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:20.839678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:23.041916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:16.394763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:18.310038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:19.733538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:21.154550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:23.382961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:16.766620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:18.693690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:19.982031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:21.548758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-29T12:58:31.164184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PassengerIdAgeSibSpParchFareSurvivedPclassSexEmbarked
PassengerId1.0000.041-0.0610.001-0.0140.1040.0320.0660.000
Age0.0411.000-0.182-0.2540.1350.1550.2690.0990.065
SibSp-0.061-0.1821.0000.4500.4470.1870.1480.2060.092
Parch0.001-0.2540.4501.0000.4100.1570.0220.2470.052
Fare-0.0140.1350.4470.4101.0000.2830.4790.1890.196
Survived0.1040.1550.1870.1570.2831.0000.3370.5400.166
Pclass0.0320.2690.1480.0220.4790.3371.0000.1300.260
Sex0.0660.0990.2060.2470.1890.5400.1301.0000.113
Embarked0.0000.0650.0920.0520.1960.1660.2600.1131.000

Missing values

2023-08-29T12:58:23.914649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T12:58:24.462832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-29T12:58:24.761621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen "Carrie"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
Test Dataset Profile Report

Overview

Dataset statistics

Number of variables11
Number of observations418
Missing cells414
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.0 KiB
Average record size in memory88.3 B

Variable types

Numeric5
Categorical6

Alerts

Name has a high cardinality: 418 distinct valuesHigh cardinality
Ticket has a high cardinality: 363 distinct valuesHigh cardinality
Cabin has a high cardinality: 76 distinct valuesHigh cardinality
Age has 86 (20.6%) missing valuesMissing
Cabin has 327 (78.2%) missing valuesMissing
PassengerId is uniformly distributedUniform
Name is uniformly distributedUniform
Ticket is uniformly distributedUniform
Cabin is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 283 (67.7%) zerosZeros
Parch has 324 (77.5%) zerosZeros

Reproduction

Analysis started2023-08-29 12:58:33.007064
Analysis finished2023-08-29 12:58:41.983236
Duration8.98 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-08-29T12:58:42.176952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityStrictly increasing
2023-08-29T12:58:42.453443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
892 1
 
0.2%
1205 1
 
0.2%
1177 1
 
0.2%
1176 1
 
0.2%
1175 1
 
0.2%
1174 1
 
0.2%
1173 1
 
0.2%
1172 1
 
0.2%
1171 1
 
0.2%
1170 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
892 1
0.2%
893 1
0.2%
894 1
0.2%
895 1
0.2%
896 1
0.2%
897 1
0.2%
898 1
0.2%
899 1
0.2%
900 1
0.2%
901 1
0.2%
ValueCountFrequency (%)
1309 1
0.2%
1308 1
0.2%
1307 1
0.2%
1306 1
0.2%
1305 1
0.2%
1304 1
0.2%
1303 1
0.2%
1302 1
0.2%
1301 1
0.2%
1300 1
0.2%

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Length

2023-08-29T12:58:42.710229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:42.961431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring characters

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Kelly, Mr. James
 
1
Carr, Miss. Jeannie
 
1
Dennis, Mr. William
 
1
Rosblom, Miss. Salli Helena
 
1
Touma, Miss. Maria Youssef
 
1
Other values (413)
413 

Length

Max length63
Median length51
Mean length27.483254
Min length13

Characters and Unicode

Total characters11488
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)100.0%

Sample

1st rowKelly, Mr. James
2nd rowWilkes, Mrs. James (Ellen Needs)
3rd rowMyles, Mr. Thomas Francis
4th rowWirz, Mr. Albert
5th rowHirvonen, Mrs. Alexander (Helga E Lindqvist)

Common Values

ValueCountFrequency (%)
Kelly, Mr. James 1
 
0.2%
Carr, Miss. Jeannie 1
 
0.2%
Dennis, Mr. William 1
 
0.2%
Rosblom, Miss. Salli Helena 1
 
0.2%
Touma, Miss. Maria Youssef 1
 
0.2%
Fleming, Miss. Honora 1
 
0.2%
Peacock, Master. Alfred Edward 1
 
0.2%
Oreskovic, Miss. Jelka 1
 
0.2%
Oxenham, Mr. Percy Thomas 1
 
0.2%
Ware, Mr. John James 1
 
0.2%
Other values (408) 408
97.6%

Length

2023-08-29T12:58:43.233872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 242
 
14.0%
miss 78
 
4.5%
mrs 72
 
4.2%
john 28
 
1.6%
william 23
 
1.3%
master 21
 
1.2%
charles 16
 
0.9%
joseph 15
 
0.9%
james 14
 
0.8%
henry 14
 
0.8%
Other values (825) 1202
69.7%

Most occurring characters

ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7395
64.4%
Uppercase Letter 1738
 
15.1%
Space Separator 1309
 
11.4%
Other Punctuation 884
 
7.7%
Open Punctuation 78
 
0.7%
Close Punctuation 78
 
0.7%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 971
13.1%
e 822
11.1%
a 786
10.6%
s 628
8.5%
i 621
8.4%
n 596
8.1%
l 526
 
7.1%
o 467
 
6.3%
t 303
 
4.1%
h 257
 
3.5%
Other values (16) 1418
19.2%
Uppercase Letter
ValueCountFrequency (%)
M 515
29.6%
J 112
 
6.4%
A 103
 
5.9%
C 101
 
5.8%
E 95
 
5.5%
S 81
 
4.7%
H 80
 
4.6%
W 76
 
4.4%
B 69
 
4.0%
L 61
 
3.5%
Other values (14) 445
25.6%
Other Punctuation
ValueCountFrequency (%)
. 418
47.3%
, 418
47.3%
" 44
 
5.0%
' 4
 
0.5%
Space Separator
ValueCountFrequency (%)
1309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9133
79.5%
Common 2355
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 971
 
10.6%
e 822
 
9.0%
a 786
 
8.6%
s 628
 
6.9%
i 621
 
6.8%
n 596
 
6.5%
l 526
 
5.8%
M 515
 
5.6%
o 467
 
5.1%
t 303
 
3.3%
Other values (40) 2898
31.7%
Common
ValueCountFrequency (%)
1309
55.6%
. 418
 
17.7%
, 418
 
17.7%
( 78
 
3.3%
) 78
 
3.3%
" 44
 
1.9%
- 6
 
0.3%
' 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
male
266 
female
152 

Length

Max length6
Median length4
Mean length4.7272727
Min length4

Characters and Unicode

Total characters1976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Length

2023-08-29T12:58:43.512960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:43.792102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Most occurring characters

ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1976
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Age
Real number (ℝ)

Distinct79
Distinct (%)23.8%
Missing86
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean30.27259
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-08-29T12:58:44.047052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile8
Q121
median27
Q339
95-th percentile57
Maximum76
Range75.83
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.181209
Coefficient of variation (CV)0.46845047
Kurtosis0.083783352
Mean30.27259
Median Absolute Deviation (MAD)8
Skewness0.45736129
Sum10050.5
Variance201.1067
MonotonicityNot monotonic
2023-08-29T12:58:44.333749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 17
 
4.1%
21 17
 
4.1%
22 16
 
3.8%
30 15
 
3.6%
18 13
 
3.1%
27 12
 
2.9%
26 12
 
2.9%
23 11
 
2.6%
25 11
 
2.6%
29 10
 
2.4%
Other values (69) 198
47.4%
(Missing) 86
20.6%
ValueCountFrequency (%)
0.17 1
 
0.2%
0.33 1
 
0.2%
0.75 1
 
0.2%
0.83 1
 
0.2%
0.92 1
 
0.2%
1 3
0.7%
2 2
0.5%
3 1
 
0.2%
5 1
 
0.2%
6 3
0.7%
ValueCountFrequency (%)
76 1
 
0.2%
67 1
 
0.2%
64 3
0.7%
63 2
0.5%
62 1
 
0.2%
61 2
0.5%
60.5 1
 
0.2%
60 3
0.7%
59 1
 
0.2%
58 1
 
0.2%

SibSp
Real number (ℝ)

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-08-29T12:58:44.591323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2023-08-29T12:58:44.796906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
8 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
5 1
 
0.2%
8 2
 
0.5%
ValueCountFrequency (%)
8 2
 
0.5%
5 1
 
0.2%
4 4
 
1.0%
3 4
 
1.0%
2 14
 
3.3%
1 110
 
26.3%
0 283
67.7%

Parch
Real number (ℝ)

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-08-29T12:58:45.037384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2023-08-29T12:58:45.241810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.5%
3 3
 
0.7%
2 33
 
7.9%
1 52
 
12.4%
0 324
77.5%

Ticket
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct363
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
PC 17608
 
5
CA. 2343
 
4
113503
 
4
PC 17483
 
3
220845
 
3
Other values (358)
399 

Length

Max length18
Median length17
Mean length6.8755981
Min length3

Characters and Unicode

Total characters2874
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)76.8%

Sample

1st row330911
2nd row363272
3rd row240276
4th row315154
5th row3101298

Common Values

ValueCountFrequency (%)
PC 17608 5
 
1.2%
CA. 2343 4
 
1.0%
113503 4
 
1.0%
PC 17483 3
 
0.7%
220845 3
 
0.7%
347077 3
 
0.7%
SOTON/O.Q. 3101315 3
 
0.7%
C.A. 31029 3
 
0.7%
16966 3
 
0.7%
230136 2
 
0.5%
Other values (353) 385
92.1%

Length

2023-08-29T12:58:45.508359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pc 32
 
5.9%
c.a 19
 
3.5%
ca 8
 
1.5%
soton/o.q 8
 
1.5%
sc/paris 7
 
1.3%
17608 5
 
0.9%
2 5
 
0.9%
a/5 5
 
0.9%
w./c 5
 
0.9%
f.c.c 4
 
0.7%
Other values (383) 445
82.0%

Most occurring characters

ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2224
77.4%
Uppercase Letter 349
 
12.1%
Other Punctuation 172
 
6.0%
Space Separator 125
 
4.3%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 92
26.4%
P 52
14.9%
A 51
14.6%
O 44
12.6%
S 40
11.5%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (5) 16
 
4.6%
Decimal Number
ValueCountFrequency (%)
3 364
16.4%
1 311
14.0%
2 268
12.1%
7 207
9.3%
6 206
9.3%
0 204
9.2%
5 195
8.8%
4 188
8.5%
8 144
 
6.5%
9 137
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
25.0%
r 1
25.0%
i 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 126
73.3%
/ 46
 
26.7%
Space Separator
ValueCountFrequency (%)
125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2521
87.7%
Latin 353
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92
26.1%
P 52
14.7%
A 51
14.4%
O 44
12.5%
S 40
11.3%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (9) 20
 
5.7%
Common
ValueCountFrequency (%)
3 364
14.4%
1 311
12.3%
2 268
10.6%
7 207
8.2%
6 206
8.2%
0 204
8.1%
5 195
7.7%
4 188
7.5%
8 144
 
5.7%
9 137
 
5.4%
Other values (3) 297
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Fare
Real number (ℝ)

Distinct169
Distinct (%)40.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean35.627188
Minimum0
Maximum512.3292
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-08-29T12:58:45.788377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2292
Q17.8958
median14.4542
Q331.5
95-th percentile151.55
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.907576
Coefficient of variation (CV)1.5692391
Kurtosis17.921595
Mean35.627188
Median Absolute Deviation (MAD)6.825
Skewness3.6872133
Sum14856.538
Variance3125.6571
MonotonicityNot monotonic
2023-08-29T12:58:46.091500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 21
 
5.0%
26 19
 
4.5%
8.05 17
 
4.1%
13 17
 
4.1%
10.5 11
 
2.6%
7.8958 11
 
2.6%
7.775 10
 
2.4%
7.2292 9
 
2.2%
7.225 9
 
2.2%
7.8542 8
 
1.9%
Other values (159) 285
68.2%
ValueCountFrequency (%)
0 2
 
0.5%
3.1708 1
 
0.2%
6.4375 2
 
0.5%
6.4958 1
 
0.2%
6.95 1
 
0.2%
7 2
 
0.5%
7.05 2
 
0.5%
7.225 9
2.2%
7.2292 9
2.2%
7.25 5
1.2%
ValueCountFrequency (%)
512.3292 1
 
0.2%
263 2
 
0.5%
262.375 5
1.2%
247.5208 1
 
0.2%
227.525 1
 
0.2%
221.7792 3
0.7%
211.5 4
1.0%
211.3375 1
 
0.2%
164.8667 2
 
0.5%
151.55 2
 
0.5%

Cabin
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct76
Distinct (%)83.5%
Missing327
Missing (%)78.2%
Memory size3.4 KiB
B57 B59 B63 B66
 
3
C89
 
2
C116
 
2
C80
 
2
C55 C57
 
2
Other values (71)
80 

Length

Max length15
Median length3
Mean length4.0769231
Min length1

Characters and Unicode

Total characters371
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)68.1%

Sample

1st rowB45
2nd rowE31
3rd rowB57 B59 B63 B66
4th rowB36
5th rowA21

Common Values

ValueCountFrequency (%)
B57 B59 B63 B66 3
 
0.7%
C89 2
 
0.5%
C116 2
 
0.5%
C80 2
 
0.5%
C55 C57 2
 
0.5%
C101 2
 
0.5%
A34 2
 
0.5%
C23 C25 C27 2
 
0.5%
C31 2
 
0.5%
F4 2
 
0.5%
Other values (66) 70
 
16.7%
(Missing) 327
78.2%

Length

2023-08-29T12:58:46.949277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 4
 
3.4%
b57 3
 
2.5%
b63 3
 
2.5%
b66 3
 
2.5%
b59 3
 
2.5%
c27 2
 
1.7%
e46 2
 
1.7%
c6 2
 
1.7%
c78 2
 
1.7%
b45 2
 
1.7%
Other values (80) 92
78.0%

Most occurring characters

ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226
60.9%
Uppercase Letter 118
31.8%
Space Separator 27
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 34
15.0%
1 33
14.6%
6 30
13.3%
3 28
12.4%
2 25
11.1%
4 21
9.3%
7 15
6.6%
8 14
6.2%
0 14
6.2%
9 12
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
68.2%
Latin 118
31.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 34
13.4%
1 33
13.0%
6 30
11.9%
3 28
11.1%
27
10.7%
2 25
9.9%
4 21
8.3%
7 15
5.9%
8 14
5.5%
0 14
5.5%
Latin
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Embarked
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
S
270 
C
102 
Q
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowS
3rd rowQ
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Length

2023-08-29T12:58:47.348840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T12:58:47.599276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 270
64.6%
c 102
 
24.4%
q 46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 418
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 418
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Interactions

2023-08-29T12:58:39.604308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:33.711966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:35.195038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:37.053792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:38.484222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:39.826014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:33.911040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:35.556812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:37.435812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:38.692112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:40.053233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:34.159351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:35.905955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:37.779429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:38.939640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:40.296925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:34.537546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:36.295774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:38.031158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:39.175558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:40.822117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:34.840330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:36.681435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:38.249620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T12:58:39.386110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-29T12:58:47.854907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PassengerIdAgeSibSpParchFarePclassSexCabinEmbarked
PassengerId1.000-0.019-0.0100.0510.0200.0540.0000.0560.060
Age-0.0191.000-0.015-0.1300.3150.3490.0000.0350.135
SibSp-0.010-0.0151.0000.4120.4410.1130.1360.0000.101
Parch0.051-0.1300.4121.0000.3780.0000.2130.0000.113
Fare0.0200.3150.4410.3781.0000.4750.1540.4160.240
Pclass0.0540.3490.1130.0000.4751.0000.1060.4130.308
Sex0.0000.0000.1360.2130.1540.1061.0000.0000.109
Cabin0.0560.0350.0000.0000.4160.4130.0001.0000.000
Embarked0.0600.1350.1010.1130.2400.3080.1090.0001.000

Missing values

2023-08-29T12:58:41.133560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T12:58:41.540976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-29T12:58:41.855333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
58973Svensson, Mr. Johan Cervinmale14.00075389.2250NaNS
68983Connolly, Miss. Katefemale30.0003309727.6292NaNQ
78992Caldwell, Mr. Albert Francismale26.01124873829.0000NaNS
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNC
99013Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNS
PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
40813003Riordan, Miss. Johanna Hannah""femaleNaN003349157.7208NaNQ
40913013Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750NaNS
41013023Naughton, Miss. HannahfemaleNaN003652377.7500NaNQ
41113031Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78Q
41213043Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750NaNS
41313053Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNS
41413061Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105C
41513073Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNS
41613083Ware, Mr. FrederickmaleNaN003593098.0500NaNS
41713093Peter, Master. Michael JmaleNaN11266822.3583NaNC